11,021 research outputs found
A multiplexed single electron transistor for application in scalable solid-state quantum computing
Single Electron Transistors (SETs) are nanoscale electrometers of
unprecedented sensitivity, and as such have been proposed as read-out devices
in a number of quantum computer architectures. We show that the functionality
of a standard SET can be multiplexed so as to operate as both read-out device
and control gate for a solid-state qubit. Multiplexing in this way may be
critical in lowering overall gate densities in scalable quantum computer
architectures.Comment: 3 pages 3 figure
Strategies and challenges to facilitate situated learning in virtual worlds post-Second Life
Virtual worlds can establish a stimulating environment to support a situated learning approach in which students simulate a task within a safe environment. While in previous years Second Life played a major role in providing such a virtual environment, there are now more and more alternative—often OpenSim-based—solutions deployed within the educational community. By drawing parallels to social networks, we discuss two aspects: how to link individually hosted virtual worlds together in order to implement context for immersion and how to identify and avoid “fake” avatars so people behind these avatars can be held accountable for their actions
Analysis and Geometric Optimization of Single Electron Transistors for Read-Out in Solid-State Quantum Computing
The single electron transistor (SET) offers unparalled opportunities as a
nano-scale electrometer, capable of measuring sub-electron charge variations.
SETs have been proposed for read-out schema in solid-state quantum computing
where quantum information processing outcomes depend on the location of a
single electron on nearby quantum dots. In this paper we investigate various
geometries of a SET in order to maximize the device's sensitivity to charge
transfer between quantum dots. Through the use of finite element modeling we
model the materials and geometries of an Al/Al2O3 SET measuring the state of
quantum dots in the Si substrate beneath. The investigation is motivated by the
quest to build a scalable quantum computer, though the methodology used is
primarily that of circuit theory. As such we provide useful techniques for any
electronic device operating at the classical/quantum interface.Comment: 13 pages, 17 figure
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Bacteria Use Type IV Pili to Walk Upright and Detach from Surfaces
1. Department of Bioengineering, California Nano Systems Institute,University of California, Los Angeles, CA 90024, USA.
2. Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX 77204, USA.
3. Department of Materials Science and Engineering, University of Illinois, Urbana-Champaign, IL 61801, USA.
4. Department of Civil Engineering and Geological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA.Bacterial biofilms are structured multicellular communities involved in a broad range of infections. Knowing how free-swimming bacteria adapt their motility mechanisms near surfaces is crucial for understanding the transition between planktonic and biofilm phenotypes. By translating microscopy movies into searchable databases of bacterial behavior, we identified fundamental type IV pili–driven mechanisms for Pseudomonas aeruginosa surface motility involved in distinct foraging strategies. Bacteria stood upright and “walked” with trajectories optimized for two-dimensional surface exploration. Vertical orientation facilitated surface
detachment and could influence biofilm morphology.Center for Nonlinear Dynamic
Gauge covariance and the fermion-photon vertex in three- and four- dimensional, massless quantum electrodynamics
In the quenched approximation, the gauge covariance properties of three
vertex Ans\"{a}tze in the Schwinger-Dyson equation for the fermion self energy
are analysed in three- and four- dimensional quantum electrodynamics. Based on
the Cornwall-Jackiw-Tomboulis effective action, it is inferred that the
spectral representation used for the vertex in the gauge technique cannot
support dynamical chiral symmetry breaking. A criterion for establishing
whether a given Ansatz can confer gauge covariance upon the Schwinger-Dyson
equation is presented and the Curtis and Pennington Ansatz is shown to satisfy
this constraint. We obtain an analytic solution of the Schwinger-Dyson equation
for quenched, massless three-dimensional quantum electrodynamics for arbitrary
values of the gauge parameter in the absence of dynamical chiral symmetry
breaking.Comment: 17 pages, PHY-7143-TH-93, REVTE
Software Extensions to UCSF Chimera for Interactive Visualization of Large Molecular Assemblies
SummaryMany structures of large molecular assemblies such as virus capsids and ribosomes have been experimentally determined to atomic resolution. We consider four software problems that arise in interactive visualization and analysis of large assemblies: how to represent multimers efficiently, how to make cartoon representations, how to calculate contacts efficiently, and how to select subassemblies. We describe techniques and algorithms we have developed and give examples of their use. Existing molecular visualization programs work well for single protein and nucleic acid molecules and for small complexes. The methods presented here are proposed as features to add to existing programs or include in next-generation visualization software to allow easy exploration of assemblies containing tens to thousands of macromolecules. Our approach is pragmatic, emphasizing simplicity of code, reliability, and speed. The methods described have been distributed as the Multiscale extension of the UCSF Chimera (www.cgl.ucsf.edu/chimera) molecular graphics program
Fracture of Solid Wood: A Review of Structure and Properties at Different Length Scales
This paper presents a review of the fracture literature of solid wood. The review is not exhaustive and is focused on the structure and properties of wood at different length scales. Fracture of wood has been examined in all pure modes as well as mixed-Mode I and II and all directions—radial, tangential, and longitudinal. The literature has been studied at a variety of levels from molecular through cellular and growth ring to macroscopic. The major conclusions are that fracture toughness perpendicular to the grain is greater than that parallel to the grain and Mode II is greater than Mode I, within a given species. Also, fracture toughness increases with increasing density and strain rate. Defects typically reduce the strength and fracture toughness, with edge defects having a greater effect. Finally, the fracture toughness of solid wood reaches a maximum between 6 to 8% moisture content. The paper discusses how these macroscopic observations are related to the chemical composition and micro/meso-structure of wood
Dynamic Analysis of Executables to Detect and Characterize Malware
It is needed to ensure the integrity of systems that process sensitive
information and control many aspects of everyday life. We examine the use of
machine learning algorithms to detect malware using the system calls generated
by executables-alleviating attempts at obfuscation as the behavior is monitored
rather than the bytes of an executable. We examine several machine learning
techniques for detecting malware including random forests, deep learning
techniques, and liquid state machines. The experiments examine the effects of
concept drift on each algorithm to understand how well the algorithms
generalize to novel malware samples by testing them on data that was collected
after the training data. The results suggest that each of the examined machine
learning algorithms is a viable solution to detect malware-achieving between
90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the
performance evaluation on an operational network may not match the performance
achieved in training. Namely, the CAA may be about the same, but the values for
precision and recall over the malware can change significantly. We structure
experiments to highlight these caveats and offer insights into expected
performance in operational environments. In addition, we use the induced models
to gain a better understanding about what differentiates the malware samples
from the goodware, which can further be used as a forensics tool to understand
what the malware (or goodware) was doing to provide directions for
investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure
Neurogenesis Deep Learning
Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference
on Neural Networks (IJCNN 2017
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